Research

Books and book chapters

Hecq, A. and E. Voisin (2023), Predicting bubble bursts in oil prices during the COVID-19 pandemic with mixed causal-noncausal models, in Advances in Econometrics in honor of Joon Y. Park.

Cubadda, G. and A. Hecq (2022), Reduced Rank Regression Models in Economics and Finance, in Oxford Research Encyclopedia of Economics and Finance, Oxford University Press.

Cubadda, G.,  Hecq A. and A. Riccardo (2019), Forecasting Realized Volatility Measures with Multivariate and Univariate Models: The Case of The US Banking Sector, in Handbook of Applied Financial Econometrics, Volume 2, Track: Financial Volatility and Covariance Modelling, edited by J. Chevallier, S. Goutte, D.Guerreiro, S. Saglio, and B. Sanhaji. Routledge, UK. 

Goetz, T., Hecq, A. and J.-P. Urbain (2013),  Testing for Common Cycles in Non-Stationary VARs with Varied Frequency data, Advances in Econometrics vol. 32.

Cubadda, G., Guardabascio, B. and A. Hecq (2013), Building a Synchronous Common Cycle Index for the EU Area,  in Cheung Y.W., and F. Westermann (Eds.), Global Interdependence, Decoupling, and Recoupling, The MIT Press, 37-52.

Centoni, M., Cubadda, G. and A. Hecq (2006), Measuring the Sources of Cyclical Fluctuations in the G7 Economies,  in Mazzi G.L., and G. Savio (Eds.), Growth and Cycle in the Euro-zone, Palgrave Macmillan, 152-159.

Hecq, A. (2000), Common Cyclical Features in Multiple Time Series and Panel Data. Methodological Aspects and Applications,  Ph.D. thesis, has received the Christian Huygens prize 2003

Articles

Hecq, A, Issler, J.V. and E. Voisin (2024), Evaluation of the credibility of the Brazilian inflation targeting system using mixed causal-noncausal models, forthcoming Journal of International Money and Finance.

Hecq, A., Issler, J. and E. Voisin (2023), An Early Warning Test for the Brazilian Inflation-Targeting Regime During the COVID-19 Pandemic, Brazilian Review of Econometrics.

Cubadda, G. Hecq, A. and E. Voisin (2023), Detecting Common Bubbles in Multivariate Mixed Causal--Noncausal Models, Econometrics.

Giancaterini, F. and A. Hecq (2023), Inference in mixed causal and noncausal models with generalized Student's t-distributions, Econometrics and Statistics.

Hecq, A., Margaritella, L. and S. Smeekes (2023), Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure, Journal of Financial Econometrics.

Giancateniri, F, Hecq, A.  and C. Morana (2022), Is climate change time reversible? in Econometrics.

Cubadda, G. and A. Hecq (2022),  Dimension Reduction for High Dimensional Vector Autoregressive Models,  in Oxford Bulletin of Economics and Statistics. 

Hecq, A., Ternes M. and I. Wilms (2022), Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions,  in Journal of Computational and Graphical Statistics.

Hecq, A. and L. Sun (2021),  Identification of Noncausal Models by Quantile Autoregressions, Studies in Nonlinear Dynamics & Econometrics.

Hecq, A. and E. Voisin (2021), Forecasting bubbles with mixed causal-noncausal autoregressive models,  Econometrics and Statistics.

Hecq, A., Issler, J. and S. Telg (2020), Mixed causal-noncausal autoregressions with exogenous regressors,  Journal of Applied Econometrics.

Dufour, J-M, Hecq, A. and A. Wan (2020), Editorial: EcoSta special issue on theoretical econometrics, Econometrics and Statistics.

Hecq, A.,  Jacobs, J.P.A.M and M. Stamatogiannis (2019), Testing for news and noise in non-stationary time series subject to multiple historical revisions,  Journal of Macroeconomics, 2019, 60, (C), 396-407.

Goetz, T. and A. Hecq (2019), Granger causality testing in mixed-frequency Vars with possibly (co)integrated processes ,  Journal of Time Series Analysis, 40: 914–935. 

Cubadda, G., Hecq, A.,  and S. Telg (2019), Detecting Co-Movements in Noncausal Time Series , Oxford Bulletin of Economics and Statistics, 2019, 81, (3), 697-715. 

Chevillon, G., Hecq, A. and S. Laurent (2018), Generating Univariate Fractional Integration within a Large VAR(1)”,  Journal of Econometrics, 204(1), 54-65. DOI: 10.1016/j.jeconom.2018.01.002

Hecq, A., Lieb, L. and S. Telg (2017),  Do Seasonal Adjustments Induce Noncausal Dynamics in Inflation Rates?,  Econometrics., , 5(4), 48

Cubadda, G., Guardabascio, B. and A. Hecq (2017), A Vector Heterogeneous Autoregressive Index Model for Realized Volatility Measures, International Journal of Forecasting, vol. 33, 2, p. 337-344.

del Barrio Castro, T. and A. Hecq (2016), Testing for Deterministic Seasonality in Mixed-Frequency VARs,  Economics Letters, 149, 20–24.  

Hecq, A., Lieb, L. and S. Telg (2016), Identification of Mixed Causal-Noncausal Models in Finite Samples,   Annals of Economics and Statistics, issue 123-124, December, p. 307-331.

Goetz, T., Hecq, A. and S. Smeekes (2016), Testing for Granger-causality in large mixed-frequency VARs,  Journal of Econometrics, 193, (2), 418-432. 

Hecq, A., Laurent, S. and F. Palm (2016), On the Univariate Representation of the BEKK Model with Common Factors, Journal of Time Series Econometrics, 8, (2), 91-113.

Goetz, T., Hecq, A. and J.-P. Urbain (2016),  Combining Forecasts from Successive Data Vintages: An Application to U.S. Growth,  International Journal of Forecasting, 32, 61-74.

Guillén, O., Hecq, A., Issler, J. and D. Saraiva (2015), Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions, International Journal of Forecasting,  31, (3), 862-875. 

Goetz, T. and A. Hecq  (2014), Nowcasting Causality in Mixed Frequency Vector Autoregressive Models,  Economics Letters, 122, (1), 74-78.

Goetz, T., Hecq, A. and J.-P. Urbain (2014), Forecasting Mixed Frequency Time Series with ECM-MIDAS Models, Journal of Forecasting, 33, (3), 198-213.

Cubadda, G., Guardabascio, B. and A. Hecq (2013),   A General to Specific Approach for Constructing Composite Business Cycle Indicators, Economic Modelling, 33,  367-374.

Hecq, A., Laurent, S. and F. Palm (2012),  Common Intraday Periodicity, Journal of Financial Econometrics.

Cubadda, G., Hecq, A. (2011), Testing for Common Autocorrelation in Data Rich Environments, Journal of Forecasting.

Cubadda, G., Hecq, A. and F. Palm (2009), Sudying Co-movements in Large Multivariate Models Without Multivariate Modelling, Journal of Econometrics., vol 148, issue 1.

Hecq, A. (2009), Asymmetric Business Cycle Co-movements, Applied Economic Letters, vol 16, issue 6 (579-584)

Cubadda, G., Hecq, A. and F. Palm (2008), Macro-panels and Reality, Economics Letters 99, (537 -540)

Centoni, M., Cubadda, G. and A. Hecq (2007), Common Shocks, Common Dynamics and the International Business Cycle, Economic Modelling, vol.24, 1, 149-166.

Hecq, A., Palm F.C. and J.P. Urbain (2006), Testing for Common Cyclical Features in VAR Models with Cointegration, 132, issue 1, 117-141 in Journal of Econometrics.

Hecq, A. (2005), Should we Really Care about Building Business Cycle Coincident Indexes!, Applied Economic Letters, 12, vol.3, 144-144

Candelon, B., Hecq, A. and W. Verschoor (2005), Measuring common cyclical features during financial turmoil: Evidence of interdependence not contagion, Journal of International Money and Finance, 24, 1317-1334.

Hecq, A. (2002), Common Cycles and Common Trends in Latin America, Medium Econometrische Toepassingen 10, 20-25.

Hecq, A., Palm, F. and J.P. Urbain (2002), , Separation, Weak Exogeneity and P-T Decompositions in Cointegrated VAR Systems with Common Features, Econometric Reviews 21, 273-307.

Cubadda, G. and A. Hecq (2001), On Non-Contemporaneous Short-Run Comovements, Economics Letters, 73, 389-397.

Hecq, A., F. Palm and J.P. Urbain (2000), Permanent-Transitory Decomposition in VAR Models with Cointegration and Common Cycles, Oxford Bulletin of Economics and Statistics 62, 511-532..

Hecq, A., F. Palm and J.P. Urbain (2000), Testing for Common Cyclical Features in Nonstationary Panel Data Models, in B.H. Baltagi (editor) Advances in Econometrics: Nonstationary Panels, Panel Cointegration and Dynamic Panels, Vol. 15, JAI Press, 131-160.

Hecq, A., F. Palm and J.P. Urbain (2000), Comovements in International Stock Markets: What can we Learn from a Common Trend-Common Cycle Analysis? , De Economist, 148, n.3, 395-406.

Candelon, B. and A. Hecq (2000), Stability of Okun’s Law in a Codependent System, Applied Economic Letters, 7, 687-693.

Beine, M., Candelon, B. and A. Hecq (2000), Determining A Perfect European Optimum Currency Area Using Common Cycle, Empirica, 27, 115-132.

Beine, M., Docquier, F. and A. Hecq (1999), Convergence des groupes en Europe: une analyse sur données régionales, Revue d’Economie Régionale et Urbaine, 45-62.

Beine, M. and A. Hecq (1999), Inference in Codependence: Some Monte Carlo Results and Applications, Annales d’Economie et de Statistique, 54, 69-90.

Hecq, A. (1998), Does Seasonal Adjustment Induce Common Cycles?, Economics Letters, 59, 289-297.

Beine, M. and A. Hecq (1998), Codependence and Convergence in the EC Economies, Journal of Policy Modeling, 20 (4), 403-426.

Beine, M. and A. Hecq (1997), Asymetric Shocks inside Future EMU, Journal of Economic Integration, 12(2), 131-140.

Hecq, A. and B. Mahy (1997),Testing for the Price and Wage Setting Model in Belgium Using Multivariate Cointegration Tests, Labour, 11, 177-199.

Hecq, A. (1996), IGARCH Effects on Autoregressive Lag Length Selection and Causality Tests, Applied Economic Letters, 3, 317-323.

Hecq, A. (1995), Unit Root Tests with Level Shift in the Presence of GARCH, Economics Letters, 49, 125-130.

Hecq, A. and J.P. Urbain (1993), Misspecification Tests, Unit Roots and Level Shifts, Economics Letters, 43, 129-135.

Hecq, A. (1992), L’impact du changement de définition de l’indice des prix de gros en Belgique sur la causalité prix de gros/prix de détail, Cahiers Economiques de Bruxelles, n°136.

Recent Discussion Papers

Cubadda, G., Giancateniri, F, Hecq, A and J. Jasiak (2023), Optimization of the Generalized Covariance estimator in multivariate mixed causal and noncausal processes.

Hecq, A., Ternes M. and I. Wilms (2023), Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions.

Hecq, A., Margaritella, L. and S. Smeekes (2023), Inference in Non-stationary High-Dimensional VARs.

Hecq, A. and D. Velasquez-Gaviria (2022), Spectral estimation for mixed causal-noncausal autoregressive models.

Routines and software packages

The MARX R package to estimate and simulate  mixed causal-noncausal models is available on CRAN: https://cran.r-project.org/web/packages/MARX/index.html.